Dewen Hu mostly deals with Neuroscience, Artificial intelligence, Pattern recognition, Brain–computer interface and Speech recognition. His study in Resting state fMRI, Default mode network, Major depressive disorder, Cognition and Neuroimaging falls under the purview of Neuroscience. The various areas that Dewen Hu examines in his Resting state fMRI study include Functional magnetic resonance imaging and Discriminative model.
His Artificial intelligence research integrates issues from Multi-objective optimization and Computer vision. His Feature extraction, Linear discriminant analysis and Support vector machine study in the realm of Pattern recognition connects with subjects such as Correlation. The study incorporates disciplines such as Stimulus, Information transfer, Column and Gaze in addition to Brain–computer interface.
His primary scientific interests are in Artificial intelligence, Neuroscience, Pattern recognition, Functional magnetic resonance imaging and Resting state fMRI. His Artificial intelligence research includes elements of Speech recognition, Brain–computer interface and Computer vision. Neuroscience is represented through his Default mode network, Neuroimaging, Cognition, Functional connectivity and Human brain research.
His work on Support vector machine, Dimensionality reduction, Principal component analysis and Canonical correlation is typically connected to Blind signal separation as part of general Pattern recognition study, connecting several disciplines of science. As part of his studies on Functional magnetic resonance imaging, Dewen Hu often connects relevant areas like Major depressive disorder. His Resting state fMRI study frequently intersects with other fields, such as Brain mapping.
His primary areas of investigation include Artificial intelligence, Pattern recognition, Neuroscience, Brain–computer interface and Electroencephalography. His work deals with themes such as Connection and Computer vision, which intersect with Artificial intelligence. His biological study deals with issues like Neuroimaging, which deal with fields such as Brain damage, Internal medicine and Cardiology.
His study in Default mode network, Resting state fMRI, Cognition, Human Connectome Project and Human brain is carried out as part of his Neuroscience studies. Dewen Hu works mostly in the field of Resting state fMRI, limiting it down to topics relating to Functional magnetic resonance imaging and, in certain cases, Connectome. In his study, Task analysis and Stimulus is strongly linked to Speech recognition, which falls under the umbrella field of Brain–computer interface.
Dewen Hu spends much of his time researching Artificial intelligence, Brain–computer interface, Neuroscience, Pattern recognition and Speech recognition. His Artificial intelligence study integrates concerns from other disciplines, such as Spatial analysis, Computer vision and Electroencephalography. His work in the fields of Brain–computer interface, such as Motor imagery, intersects with other areas such as Wheelchair.
His Pattern recognition study combines topics in areas such as Interpretability, Deep learning, DUAL and Sensitivity. The Speech recognition study combines topics in areas such as Auditory stimuli and Significant difference. As a member of one scientific family, he mostly works in the field of Default mode network, focusing on Human brain and, on occasion, Discriminative model.
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Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis
Ling Li Zeng;Hui Shen;Li Liu;Lubin Wang.
Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI.
Hui Shen;Lubin Wang;Yadong Liu;Dewen Hu.
Rapid and brief communication: Two-dimensional locality preserving projections (2DLPP) with its application to palmprint recognition
Dewen Hu;Guiyu Feng;Zongtan Zhou.
Pattern Recognition (2007)
Reply: Comment on two-dimensional locality preserving projections (2DLPP) with its application to palmprint recognition
Dewen Hu;Guiyu Feng;Zongtan Zhou.
Pattern Recognition (2008)
A Treatment-Resistant Default Mode Subnetwork in Major Depression
Baojuan Li;Li Liu;Karl J. Friston;Hui Shen.
Biological Psychiatry (2013)
Neurobiological basis of head motion in brain imaging
Ling-Li Zeng;Ling-Li Zeng;Danhong Wang;Michael D. Fox;Michael D. Fox;Mert Sabuncu.
Proceedings of the National Academy of Sciences of the United States of America (2014)
A novel hybrid BCI speller based on the incorporation of SSVEP into the P300 paradigm.
Erwei Yin;Zongtan Zhou;Jun Jiang;Fanglin Chen.
Journal of Neural Engineering (2013)
Multiobjective Reinforcement Learning: A Comprehensive Overview
Chunming Liu;Xin Xu;Dewen Hu.
systems man and cybernetics (2015)
A Dynamically Optimized SSVEP Brain–Computer Interface (BCI) Speller
Erwei Yin;Zongtan Zhou;Jun Jiang;Yang Yu.
IEEE Transactions on Biomedical Engineering (2015)
Gray matter density reduction in the insula in fire survivors with posttraumatic stress disorder: A voxel-based morphometric study
Shulin Chen;Weiwei Xia;Lingjiang Li;Jun Liu.
Psychiatry Research-neuroimaging (2006)
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